An LPC Pole Processing Method for Enhancing the Identification of Dominant Spectral Features

This paper proposes a new time-resolved spectral analysis method based on a modification to the Linear Predictive Coding (LPC) method for enhancing the identification of the dominant frequencies of a signal. The method described here is based on a z-plane analysis of the LPC poles. These poles are used to produce a series of reduced order filter transfer functions which can accurately identify and estimate the frequency of the dominant spectral features. The standard LPC method has been shown to suffer from a sensitivity to noise and its performance is dependent on the filter order. The proposed method can accurately identify the dominant frequency components over a range of filter orders and is shown to be robust in the presence of noise. Compared with traditional time-resolved methods, it is a parameterized method where the identification of the dominant frequency changes can be directly obtained in the form of frequency measurements. In a series of 10,000 Monte Carlo experiments on a single component and multiple component signals, this LPC Pole Processing (LPC-PP) method outperforms the standard LPC method by accurately identifying the dominant frequency components in the signals